Overview

Dataset statistics

Number of variables36
Number of observations180519
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.6 MiB
Average record size in memory288.0 B

Variable types

Categorical22
Numeric14

Alerts

product_status has constant value ""Constant
customer_city has a high cardinality: 563 distinct valuesHigh cardinality
order_city has a high cardinality: 3597 distinct valuesHigh cardinality
order_country has a high cardinality: 164 distinct valuesHigh cardinality
order_date has a high cardinality: 65752 distinct valuesHigh cardinality
order_state has a high cardinality: 1089 distinct valuesHigh cardinality
product_name has a high cardinality: 118 distinct valuesHigh cardinality
shipping_date has a high cardinality: 63701 distinct valuesHigh cardinality
shipping_days_real is highly overall correlated with shipping_days_scheduled and 3 other fieldsHigh correlation
profit_per_order is highly overall correlated with order_item_profit_ratioHigh correlation
sales_per_customer is highly overall correlated with sales and 1 other fieldsHigh correlation
category_id is highly overall correlated with department_id and 3 other fieldsHigh correlation
customer_id is highly overall correlated with category_nameHigh correlation
department_id is highly overall correlated with category_id and 3 other fieldsHigh correlation
order_id is highly overall correlated with order_item_id and 2 other fieldsHigh correlation
order_item_discount is highly overall correlated with order_item_discount_rate and 1 other fieldsHigh correlation
order_item_discount_rate is highly overall correlated with order_item_discountHigh correlation
order_item_id is highly overall correlated with order_id and 2 other fieldsHigh correlation
order_item_profit_ratio is highly overall correlated with profit_per_orderHigh correlation
sales is highly overall correlated with sales_per_customer and 3 other fieldsHigh correlation
product_id_card is highly overall correlated with category_id and 3 other fieldsHigh correlation
product_price is highly overall correlated with sales_per_customer and 2 other fieldsHigh correlation
type is highly overall correlated with order_statusHigh correlation
shipping_days_scheduled is highly overall correlated with shipping_days_real and 1 other fieldsHigh correlation
delivery_status is highly overall correlated with shipping_days_real and 2 other fieldsHigh correlation
late_delivery_risk is highly overall correlated with shipping_days_real and 1 other fieldsHigh correlation
category_name is highly overall correlated with category_id and 6 other fieldsHigh correlation
customer_country is highly overall correlated with customer_stateHigh correlation
customer_state is highly overall correlated with customer_countryHigh correlation
department_name is highly overall correlated with category_id and 3 other fieldsHigh correlation
market is highly overall correlated with order_id and 2 other fieldsHigh correlation
order_region is highly overall correlated with order_id and 2 other fieldsHigh correlation
order_status is highly overall correlated with type and 1 other fieldsHigh correlation
shipping_mode is highly overall correlated with shipping_days_real and 1 other fieldsHigh correlation
order_date is uniformly distributedUniform
order_item_id is uniformly distributedUniform
shipping_date is uniformly distributedUniform
order_item_id has unique valuesUnique
shipping_days_real has 5080 (2.8%) zerosZeros
order_item_discount has 10028 (5.6%) zerosZeros
order_item_discount_rate has 10028 (5.6%) zerosZeros

Reproduction

Analysis started2023-04-30 10:29:27.261934
Analysis finished2023-04-30 10:30:18.325188
Duration51.06 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
DEBIT
69295 
TRANSFER
49883 
PAYMENT
41725 
CASH
19616 

Length

Max length8
Median length7
Mean length6.1826068
Min length4

Characters and Unicode

Total characters1116078
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEBIT
2nd rowTRANSFER
3rd rowCASH
4th rowDEBIT
5th rowPAYMENT

Common Values

ValueCountFrequency (%)
DEBIT 69295
38.4%
TRANSFER 49883
27.6%
PAYMENT 41725
23.1%
CASH 19616
 
10.9%

Length

2023-04-30T12:30:18.680266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-30T12:30:18.828311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
debit 69295
38.4%
transfer 49883
27.6%
payment 41725
23.1%
cash 19616
 
10.9%

Most occurring characters

ValueCountFrequency (%)
E 160903
14.4%
T 160903
14.4%
A 111224
10.0%
R 99766
8.9%
N 91608
8.2%
S 69499
6.2%
D 69295
6.2%
B 69295
6.2%
I 69295
6.2%
F 49883
 
4.5%
Other values (5) 164407
14.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1116078
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 160903
14.4%
T 160903
14.4%
A 111224
10.0%
R 99766
8.9%
N 91608
8.2%
S 69499
6.2%
D 69295
6.2%
B 69295
6.2%
I 69295
6.2%
F 49883
 
4.5%
Other values (5) 164407
14.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1116078
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 160903
14.4%
T 160903
14.4%
A 111224
10.0%
R 99766
8.9%
N 91608
8.2%
S 69499
6.2%
D 69295
6.2%
B 69295
6.2%
I 69295
6.2%
F 49883
 
4.5%
Other values (5) 164407
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1116078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 160903
14.4%
T 160903
14.4%
A 111224
10.0%
R 99766
8.9%
N 91608
8.2%
S 69499
6.2%
D 69295
6.2%
B 69295
6.2%
I 69295
6.2%
F 49883
 
4.5%
Other values (5) 164407
14.7%

shipping_days_real
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.497654
Minimum0
Maximum6
Zeros5080
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-30T12:30:18.935334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6237218
Coefficient of variation (CV)0.46423169
Kurtosis-1.0079136
Mean3.497654
Median Absolute Deviation (MAD)1
Skewness0.084771273
Sum631393
Variance2.6364726
MonotonicityNot monotonic
2023-04-30T12:30:19.031356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 56618
31.4%
3 28765
15.9%
6 28723
15.9%
4 28513
15.8%
5 28163
15.6%
0 5080
 
2.8%
1 4657
 
2.6%
ValueCountFrequency (%)
0 5080
 
2.8%
1 4657
 
2.6%
2 56618
31.4%
3 28765
15.9%
4 28513
15.8%
5 28163
15.6%
6 28723
15.9%
ValueCountFrequency (%)
6 28723
15.9%
5 28163
15.6%
4 28513
15.8%
3 28765
15.9%
2 56618
31.4%
1 4657
 
2.6%
0 5080
 
2.8%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
4
107752 
2
35216 
1
27814 
0
 
9737

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180519
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

Length

2023-04-30T12:30:19.148384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-30T12:30:19.284410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

Most occurring characters

ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 180519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 107752
59.7%
2 35216
 
19.5%
1 27814
 
15.4%
0 9737
 
5.4%

profit_per_order
Real number (ℝ)

Distinct21998
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.974989
Minimum-4274.98
Maximum911.79999
Zeros1177
Zeros (%)0.7%
Negative33784
Negative (%)18.7%
Memory size1.4 MiB
2023-04-30T12:30:19.418452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-4274.98
5-th percentile-139.251
Q17
median31.52
Q364.800003
95-th percentile132.28999
Maximum911.79999
Range5186.78
Interquartile range (IQR)57.800003

Descriptive statistics

Standard deviation104.43353
Coefficient of variation (CV)4.7523813
Kurtosis71.377259
Mean21.974989
Median Absolute Deviation (MAD)27.88
Skewness-4.7418341
Sum3966903
Variance10906.361
MonotonicityNot monotonic
2023-04-30T12:30:19.577482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1177
 
0.7%
143.9900055 199
 
0.1%
72 194
 
0.1%
46.79999924 188
 
0.1%
24 181
 
0.1%
18 175
 
0.1%
63.70000076 172
 
0.1%
62.40000153 168
 
0.1%
12 166
 
0.1%
14.39999962 166
 
0.1%
Other values (21988) 177733
98.5%
ValueCountFrequency (%)
-4274.97998 1
< 0.1%
-3442.5 1
< 0.1%
-3366 1
< 0.1%
-3000 1
< 0.1%
-2592 1
< 0.1%
-2550 1
< 0.1%
-2351.25 1
< 0.1%
-2328 1
< 0.1%
-2280 1
< 0.1%
-2255.25 1
< 0.1%
ValueCountFrequency (%)
911.7999878 1
< 0.1%
864 1
< 0.1%
721.5999756 1
< 0.1%
720.2999878 1
< 0.1%
720 2
< 0.1%
712.9500122 1
< 0.1%
708.75 1
< 0.1%
705.5999756 2
< 0.1%
705 1
< 0.1%
698.4000244 2
< 0.1%

sales_per_customer
Real number (ℝ)

Distinct2927
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.10761
Minimum7.4899998
Maximum1939.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-30T12:30:19.736514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7.4899998
5-th percentile41.5
Q1104.38
median163.99001
Q3247.39999
95-th percentile383.98001
Maximum1939.99
Range1932.5
Interquartile range (IQR)143.02

Descriptive statistics

Standard deviation120.04367
Coefficient of variation (CV)0.65559084
Kurtosis23.920362
Mean183.10761
Median Absolute Deviation (MAD)67.000008
Skewness2.8884461
Sum33054402
Variance14410.483
MonotonicityNot monotonic
2023-04-30T12:30:19.891540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122.8399963 1264
 
0.7%
109.1900024 1247
 
0.7%
124.7900009 1243
 
0.7%
129.9900055 1243
 
0.7%
116.9899979 1243
 
0.7%
123.4899979 1243
 
0.7%
120.8899994 1243
 
0.7%
127.3899994 1243
 
0.7%
97.48999786 1243
 
0.7%
118.2900009 1243
 
0.7%
Other values (2917) 168064
93.1%
ValueCountFrequency (%)
7.489999771 3
 
< 0.1%
7.989999771 3
 
< 0.1%
8.18999958 3
 
< 0.1%
8.289999962 3
 
< 0.1%
8.390000343 3
 
< 0.1%
8.470000267 15
< 0.1%
8.489999771 3
 
< 0.1%
8.659999847 29
< 0.1%
8.68999958 3
 
< 0.1%
8.789999962 3
 
< 0.1%
ValueCountFrequency (%)
1939.98999 1
< 0.1%
1919.98999 1
< 0.1%
1899.98999 1
< 0.1%
1889.98999 1
< 0.1%
1859.98999 1
< 0.1%
1819.98999 1
< 0.1%
1799.98999 1
< 0.1%
1759.98999 1
< 0.1%
1739.98999 1
< 0.1%
1699.98999 1
< 0.1%

delivery_status
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Late delivery
98977 
Advance shipping
41592 
Shipping on time
32196 
Shipping canceled
 
7754

Length

Max length17
Median length13
Mean length14.39808
Min length13

Characters and Unicode

Total characters2599127
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdvance shipping
2nd rowLate delivery
3rd rowShipping on time
4th rowAdvance shipping
5th rowAdvance shipping

Common Values

ValueCountFrequency (%)
Late delivery 98977
54.8%
Advance shipping 41592
23.0%
Shipping on time 32196
 
17.8%
Shipping canceled 7754
 
4.3%

Length

2023-04-30T12:30:20.047584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-30T12:30:20.189616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
late 98977
25.2%
delivery 98977
25.2%
shipping 81542
20.7%
advance 41592
10.6%
on 32196
 
8.2%
time 32196
 
8.2%
canceled 7754
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e 386227
14.9%
i 294257
11.3%
212715
 
8.2%
n 163084
 
6.3%
p 163084
 
6.3%
d 148323
 
5.7%
a 148323
 
5.7%
v 140569
 
5.4%
t 131173
 
5.0%
l 106731
 
4.1%
Other values (11) 704641
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2205893
84.9%
Space Separator 212715
 
8.2%
Uppercase Letter 180519
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 386227
17.5%
i 294257
13.3%
n 163084
 
7.4%
p 163084
 
7.4%
d 148323
 
6.7%
a 148323
 
6.7%
v 140569
 
6.4%
t 131173
 
5.9%
l 106731
 
4.8%
y 98977
 
4.5%
Other values (7) 425145
19.3%
Uppercase Letter
ValueCountFrequency (%)
L 98977
54.8%
A 41592
23.0%
S 39950
22.1%
Space Separator
ValueCountFrequency (%)
212715
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2386412
91.8%
Common 212715
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 386227
16.2%
i 294257
12.3%
n 163084
 
6.8%
p 163084
 
6.8%
d 148323
 
6.2%
a 148323
 
6.2%
v 140569
 
5.9%
t 131173
 
5.5%
l 106731
 
4.5%
L 98977
 
4.1%
Other values (10) 605664
25.4%
Common
ValueCountFrequency (%)
212715
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2599127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 386227
14.9%
i 294257
11.3%
212715
 
8.2%
n 163084
 
6.3%
p 163084
 
6.3%
d 148323
 
5.7%
a 148323
 
5.7%
v 140569
 
5.4%
t 131173
 
5.0%
l 106731
 
4.1%
Other values (11) 704641
27.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
98977 
0
81542 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180519
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

Length

2023-04-30T12:30:20.309644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-30T12:30:20.432671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

Most occurring characters

ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

Most occurring scripts

ValueCountFrequency (%)
Common 180519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 98977
54.8%
0 81542
45.2%

category_id
Real number (ℝ)

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.851451
Minimum2
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-30T12:30:20.558690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q118
median29
Q345
95-th percentile48
Maximum76
Range74
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.640064
Coefficient of variation (CV)0.49103145
Kurtosis-0.60326101
Mean31.851451
Median Absolute Deviation (MAD)14
Skewness0.3616248
Sum5749792
Variance244.6116
MonotonicityNot monotonic
2023-04-30T12:30:20.709723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 24551
13.6%
18 22246
12.3%
24 21035
11.7%
46 19298
10.7%
45 17325
9.6%
48 15540
8.6%
43 13729
7.6%
9 12487
6.9%
29 10984
6.1%
37 2029
 
1.1%
Other values (41) 21295
11.8%
ValueCountFrequency (%)
2 138
 
0.1%
3 632
 
0.4%
4 67
 
< 0.1%
5 343
 
0.2%
6 328
 
0.2%
7 614
 
0.3%
9 12487
6.9%
10 111
 
0.1%
11 309
 
0.2%
12 423
 
0.2%
ValueCountFrequency (%)
76 650
0.4%
75 838
0.5%
74 529
0.3%
73 357
0.2%
72 492
0.3%
71 434
0.2%
70 208
 
0.1%
69 362
0.2%
68 484
0.3%
67 483
0.3%

category_name
Categorical

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Cleats
24551 
Men's Footwear
22246 
Women's Apparel
21035 
Indoor/Outdoor Games
19298 
Fishing
17325 
Other values (45)
76064 

Length

Max length20
Median length17
Mean length12.707793
Min length4

Characters and Unicode

Total characters2293998
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSporting Goods
2nd rowSporting Goods
3rd rowSporting Goods
4th rowSporting Goods
5th rowSporting Goods

Common Values

ValueCountFrequency (%)
Cleats 24551
13.6%
Men's Footwear 22246
12.3%
Women's Apparel 21035
11.7%
Indoor/Outdoor Games 19298
10.7%
Fishing 17325
9.6%
Water Sports 15540
8.6%
Camping & Hiking 13729
7.6%
Cardio Equipment 12487
6.9%
Shop By Sport 10984
6.1%
Electronics 3156
 
1.7%
Other values (40) 20168
11.2%

Length

2023-04-30T12:30:20.869758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cleats 24551
 
7.3%
men's 22737
 
6.8%
apparel 22677
 
6.8%
footwear 22246
 
6.6%
women's 21866
 
6.5%
games 20136
 
6.0%
indoor/outdoor 19298
 
5.7%
fishing 17325
 
5.2%
15613
 
4.7%
water 15540
 
4.6%
Other values (60) 133774
39.8%

Most occurring characters

ValueCountFrequency (%)
o 213963
 
9.3%
e 180726
 
7.9%
156787
 
6.8%
r 150127
 
6.5%
s 145604
 
6.3%
a 140194
 
6.1%
n 132705
 
5.8%
t 130741
 
5.7%
i 114517
 
5.0%
p 110861
 
4.8%
Other values (39) 817773
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1712275
74.6%
Uppercase Letter 342143
 
14.9%
Space Separator 156787
 
6.8%
Other Punctuation 81819
 
3.6%
Dash Punctuation 974
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 213963
12.5%
e 180726
10.6%
r 150127
8.8%
s 145604
8.5%
a 140194
8.2%
n 132705
7.8%
t 130741
7.6%
i 114517
 
6.7%
p 110861
 
6.5%
m 69683
 
4.1%
Other values (14) 323154
18.9%
Uppercase Letter
ValueCountFrequency (%)
C 56058
16.4%
S 40270
11.8%
F 39880
11.7%
W 37406
10.9%
G 27667
8.1%
A 25257
7.4%
M 24017
7.0%
I 20272
 
5.9%
O 19298
 
5.6%
E 16074
 
4.7%
Other values (9) 35944
10.5%
Other Punctuation
ValueCountFrequency (%)
' 46840
57.2%
/ 19298
23.6%
& 15613
 
19.1%
! 68
 
0.1%
Space Separator
ValueCountFrequency (%)
156787
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 974
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2054418
89.6%
Common 239580
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 213963
 
10.4%
e 180726
 
8.8%
r 150127
 
7.3%
s 145604
 
7.1%
a 140194
 
6.8%
n 132705
 
6.5%
t 130741
 
6.4%
i 114517
 
5.6%
p 110861
 
5.4%
m 69683
 
3.4%
Other values (33) 665297
32.4%
Common
ValueCountFrequency (%)
156787
65.4%
' 46840
 
19.6%
/ 19298
 
8.1%
& 15613
 
6.5%
- 974
 
0.4%
! 68
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2293998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 213963
 
9.3%
e 180726
 
7.9%
156787
 
6.8%
r 150127
 
6.5%
s 145604
 
6.3%
a 140194
 
6.1%
n 132705
 
5.8%
t 130741
 
5.7%
i 114517
 
5.0%
p 110861
 
4.8%
Other values (39) 817773
35.6%

customer_city
Categorical

Distinct563
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Caguas
66770 
Chicago
 
3885
Los Angeles
 
3417
Brooklyn
 
3412
New York
 
1816
Other values (558)
101219 

Length

Max length20
Median length19
Mean length7.7086235
Min length2

Characters and Unicode

Total characters1391553
Distinct characters52
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaguas
2nd rowCaguas
3rd rowSan Jose
4th rowLos Angeles
5th rowCaguas

Common Values

ValueCountFrequency (%)
Caguas 66770
37.0%
Chicago 3885
 
2.2%
Los Angeles 3417
 
1.9%
Brooklyn 3412
 
1.9%
New York 1816
 
1.0%
Philadelphia 1577
 
0.9%
Bronx 1500
 
0.8%
San Diego 1437
 
0.8%
Miami 1314
 
0.7%
Houston 1297
 
0.7%
Other values (553) 94094
52.1%

Length

2023-04-30T12:30:21.023807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
caguas 66770
30.8%
san 5129
 
2.4%
chicago 3937
 
1.8%
los 3417
 
1.6%
angeles 3417
 
1.6%
brooklyn 3412
 
1.6%
new 3107
 
1.4%
york 2165
 
1.0%
beach 2084
 
1.0%
city 1659
 
0.8%
Other values (585) 121822
56.2%

Most occurring characters

ValueCountFrequency (%)
a 228937
16.5%
s 111699
 
8.0%
u 89280
 
6.4%
g 89233
 
6.4%
o 85515
 
6.1%
C 82588
 
5.9%
e 81172
 
5.8%
n 76231
 
5.5%
l 60937
 
4.4%
i 60636
 
4.4%
Other values (42) 425325
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1138231
81.8%
Uppercase Letter 216922
 
15.6%
Space Separator 36400
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 228937
20.1%
s 111699
9.8%
u 89280
 
7.8%
g 89233
 
7.8%
o 85515
 
7.5%
e 81172
 
7.1%
n 76231
 
6.7%
l 60937
 
5.4%
i 60636
 
5.3%
r 57370
 
5.0%
Other values (16) 197221
17.3%
Uppercase Letter
ValueCountFrequency (%)
C 82588
38.1%
S 14019
 
6.5%
B 13944
 
6.4%
L 13048
 
6.0%
A 10848
 
5.0%
P 10565
 
4.9%
M 9378
 
4.3%
H 8518
 
3.9%
D 5758
 
2.7%
N 5664
 
2.6%
Other values (15) 42592
19.6%
Space Separator
ValueCountFrequency (%)
36400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1355153
97.4%
Common 36400
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 228937
16.9%
s 111699
 
8.2%
u 89280
 
6.6%
g 89233
 
6.6%
o 85515
 
6.3%
C 82588
 
6.1%
e 81172
 
6.0%
n 76231
 
5.6%
l 60937
 
4.5%
i 60636
 
4.5%
Other values (41) 388925
28.7%
Common
ValueCountFrequency (%)
36400
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1391553
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 228937
16.5%
s 111699
 
8.0%
u 89280
 
6.4%
g 89233
 
6.4%
o 85515
 
6.1%
C 82588
 
5.9%
e 81172
 
5.8%
n 76231
 
5.5%
l 60937
 
4.4%
i 60636
 
4.4%
Other values (42) 425325
30.6%

customer_country
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
EE. UU.
111146 
Puerto Rico
69373 

Length

Max length11
Median length7
Mean length8.53719
Min length7

Characters and Unicode

Total characters1541125
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPuerto Rico
2nd rowPuerto Rico
3rd rowEE. UU.
4th rowEE. UU.
5th rowPuerto Rico

Common Values

ValueCountFrequency (%)
EE. UU. 111146
61.6%
Puerto Rico 69373
38.4%

Length

2023-04-30T12:30:21.161866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-30T12:30:21.304888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
ee 111146
30.8%
uu 111146
30.8%
puerto 69373
19.2%
rico 69373
19.2%

Most occurring characters

ValueCountFrequency (%)
E 222292
14.4%
. 222292
14.4%
U 222292
14.4%
180519
11.7%
o 138746
9.0%
P 69373
 
4.5%
u 69373
 
4.5%
e 69373
 
4.5%
r 69373
 
4.5%
t 69373
 
4.5%
Other values (3) 208119
13.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 583330
37.9%
Lowercase Letter 554984
36.0%
Other Punctuation 222292
 
14.4%
Space Separator 180519
 
11.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 138746
25.0%
u 69373
12.5%
e 69373
12.5%
r 69373
12.5%
t 69373
12.5%
i 69373
12.5%
c 69373
12.5%
Uppercase Letter
ValueCountFrequency (%)
E 222292
38.1%
U 222292
38.1%
P 69373
 
11.9%
R 69373
 
11.9%
Other Punctuation
ValueCountFrequency (%)
. 222292
100.0%
Space Separator
ValueCountFrequency (%)
180519
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1138314
73.9%
Common 402811
 
26.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 222292
19.5%
U 222292
19.5%
o 138746
12.2%
P 69373
 
6.1%
u 69373
 
6.1%
e 69373
 
6.1%
r 69373
 
6.1%
t 69373
 
6.1%
R 69373
 
6.1%
i 69373
 
6.1%
Common
ValueCountFrequency (%)
. 222292
55.2%
180519
44.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1541125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 222292
14.4%
. 222292
14.4%
U 222292
14.4%
180519
11.7%
o 138746
9.0%
P 69373
 
4.5%
u 69373
 
4.5%
e 69373
 
4.5%
r 69373
 
4.5%
t 69373
 
4.5%
Other values (3) 208119
13.5%

customer_id
Real number (ℝ)

Distinct20652
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6691.3795
Minimum1
Maximum20757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-30T12:30:21.433916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile649
Q13258.5
median6457
Q39779
95-th percentile12383
Maximum20757
Range20756
Interquartile range (IQR)6520.5

Descriptive statistics

Standard deviation4162.9181
Coefficient of variation (CV)0.62213152
Kurtosis0.014898822
Mean6691.3795
Median Absolute Deviation (MAD)3263
Skewness0.48876825
Sum1.2079211 × 109
Variance17329887
MonotonicityNot monotonic
2023-04-30T12:30:21.581957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5654 47
 
< 0.1%
10591 45
 
< 0.1%
5004 45
 
< 0.1%
5715 44
 
< 0.1%
3708 44
 
< 0.1%
9371 44
 
< 0.1%
1443 43
 
< 0.1%
791 43
 
< 0.1%
12284 43
 
< 0.1%
2641 43
 
< 0.1%
Other values (20642) 180078
99.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 10
< 0.1%
3 18
< 0.1%
4 14
< 0.1%
5 7
 
< 0.1%
6 15
< 0.1%
7 22
< 0.1%
8 19
< 0.1%
9 14
< 0.1%
10 8
 
< 0.1%
ValueCountFrequency (%)
20757 1
< 0.1%
20756 1
< 0.1%
20755 1
< 0.1%
20754 1
< 0.1%
20753 1
< 0.1%
20752 1
< 0.1%
20751 1
< 0.1%
20750 1
< 0.1%
20749 1
< 0.1%
20748 1
< 0.1%

customer_segment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Consumer
93504 
Corporate
54789 
Home Office
32226 

Length

Max length11
Median length8
Mean length8.839064
Min length8

Characters and Unicode

Total characters1595619
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer
2nd rowConsumer
3rd rowConsumer
4th rowHome Office
5th rowCorporate

Common Values

ValueCountFrequency (%)
Consumer 93504
51.8%
Corporate 54789
30.4%
Home Office 32226
 
17.9%

Length

2023-04-30T12:30:21.713986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-30T12:30:21.848019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
consumer 93504
44.0%
corporate 54789
25.8%
home 32226
 
15.1%
office 32226
 
15.1%

Most occurring characters

ValueCountFrequency (%)
o 235308
14.7%
e 212745
13.3%
r 203082
12.7%
C 148293
9.3%
m 125730
7.9%
n 93504
 
5.9%
s 93504
 
5.9%
u 93504
 
5.9%
f 64452
 
4.0%
t 54789
 
3.4%
Other values (7) 270708
17.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1350648
84.6%
Uppercase Letter 212745
 
13.3%
Space Separator 32226
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 235308
17.4%
e 212745
15.8%
r 203082
15.0%
m 125730
9.3%
n 93504
 
6.9%
s 93504
 
6.9%
u 93504
 
6.9%
f 64452
 
4.8%
t 54789
 
4.1%
p 54789
 
4.1%
Other values (3) 119241
8.8%
Uppercase Letter
ValueCountFrequency (%)
C 148293
69.7%
H 32226
 
15.1%
O 32226
 
15.1%
Space Separator
ValueCountFrequency (%)
32226
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1563393
98.0%
Common 32226
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 235308
15.1%
e 212745
13.6%
r 203082
13.0%
C 148293
9.5%
m 125730
8.0%
n 93504
 
6.0%
s 93504
 
6.0%
u 93504
 
6.0%
f 64452
 
4.1%
t 54789
 
3.5%
Other values (6) 238482
15.3%
Common
ValueCountFrequency (%)
32226
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1595619
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 235308
14.7%
e 212745
13.3%
r 203082
12.7%
C 148293
9.3%
m 125730
7.9%
n 93504
 
5.9%
s 93504
 
5.9%
u 93504
 
5.9%
f 64452
 
4.0%
t 54789
 
3.4%
Other values (7) 270708
17.0%

customer_state
Categorical

Distinct46
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
PR
69373 
CA
29223 
NY
11327 
TX
9103 
IL
7631 
Other values (41)
53862 

Length

Max length5
Median length2
Mean length2.0000499
Min length2

Characters and Unicode

Total characters361047
Distinct characters31
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPR
2nd rowPR
3rd rowCA
4th rowCA
5th rowPR

Common Values

ValueCountFrequency (%)
PR 69373
38.4%
CA 29223
16.2%
NY 11327
 
6.3%
TX 9103
 
5.0%
IL 7631
 
4.2%
FL 5456
 
3.0%
OH 4095
 
2.3%
PA 3824
 
2.1%
MI 3804
 
2.1%
NJ 3191
 
1.8%
Other values (36) 33492
18.6%

Length

2023-04-30T12:30:21.966042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pr 69373
38.4%
ca 29223
16.2%
ny 11327
 
6.3%
tx 9103
 
5.0%
il 7631
 
4.2%
fl 5456
 
3.0%
oh 4095
 
2.3%
pa 3824
 
2.1%
mi 3804
 
2.1%
nj 3191
 
1.8%
Other values (36) 33492
18.6%

Most occurring characters

ValueCountFrequency (%)
P 73197
20.3%
R 71448
19.8%
A 44166
12.2%
C 35467
9.8%
N 21949
 
6.1%
I 14591
 
4.0%
L 14070
 
3.9%
T 12834
 
3.6%
Y 11814
 
3.3%
M 10888
 
3.0%
Other values (21) 50623
14.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 361032
> 99.9%
Decimal Number 15
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 73197
20.3%
R 71448
19.8%
A 44166
12.2%
C 35467
9.8%
N 21949
 
6.1%
I 14591
 
4.0%
L 14070
 
3.9%
T 12834
 
3.6%
Y 11814
 
3.3%
M 10888
 
3.0%
Other values (14) 50608
14.0%
Decimal Number
ValueCountFrequency (%)
5 4
26.7%
9 3
20.0%
7 3
20.0%
8 2
13.3%
1 1
 
6.7%
3 1
 
6.7%
2 1
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 361032
> 99.9%
Common 15
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 73197
20.3%
R 71448
19.8%
A 44166
12.2%
C 35467
9.8%
N 21949
 
6.1%
I 14591
 
4.0%
L 14070
 
3.9%
T 12834
 
3.6%
Y 11814
 
3.3%
M 10888
 
3.0%
Other values (14) 50608
14.0%
Common
ValueCountFrequency (%)
5 4
26.7%
9 3
20.0%
7 3
20.0%
8 2
13.3%
1 1
 
6.7%
3 1
 
6.7%
2 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 361047
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 73197
20.3%
R 71448
19.8%
A 44166
12.2%
C 35467
9.8%
N 21949
 
6.1%
I 14591
 
4.0%
L 14070
 
3.9%
T 12834
 
3.6%
Y 11814
 
3.3%
M 10888
 
3.0%
Other values (21) 50623
14.0%

department_id
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4434602
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-30T12:30:22.084071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q37
95-th percentile7
Maximum12
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.629246
Coefficient of variation (CV)0.29930338
Kurtosis-0.18169651
Mean5.4434602
Median Absolute Deviation (MAD)1
Skewness0.27332063
Sum982648
Variance2.6544426
MonotonicityNot monotonic
2023-04-30T12:30:22.201087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7 66861
37.0%
4 48998
27.1%
5 33220
18.4%
3 14525
 
8.0%
6 9686
 
5.4%
2 2479
 
1.4%
9 2026
 
1.1%
10 1465
 
0.8%
11 492
 
0.3%
8 405
 
0.2%
ValueCountFrequency (%)
2 2479
 
1.4%
3 14525
 
8.0%
4 48998
27.1%
5 33220
18.4%
6 9686
 
5.4%
7 66861
37.0%
8 405
 
0.2%
9 2026
 
1.1%
10 1465
 
0.8%
11 492
 
0.3%
ValueCountFrequency (%)
12 362
 
0.2%
11 492
 
0.3%
10 1465
 
0.8%
9 2026
 
1.1%
8 405
 
0.2%
7 66861
37.0%
6 9686
 
5.4%
5 33220
18.4%
4 48998
27.1%
3 14525
 
8.0%

department_name
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Fan Shop
66861 
Apparel
48998 
Golf
33220 
Footwear
14525 
Outdoors
9686 
Other values (6)
7229 

Length

Max length18
Median length8
Mean length7.0397133
Min length4

Characters and Unicode

Total characters1270802
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFitness
2nd rowFitness
3rd rowFitness
4th rowFitness
5th rowFitness

Common Values

ValueCountFrequency (%)
Fan Shop 66861
37.0%
Apparel 48998
27.1%
Golf 33220
18.4%
Footwear 14525
 
8.0%
Outdoors 9686
 
5.4%
Fitness 2479
 
1.4%
Discs Shop 2026
 
1.1%
Technology 1465
 
0.8%
Pet Shop 492
 
0.3%
Book Shop 405
 
0.2%

Length

2023-04-30T12:30:22.334119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
shop 69784
27.8%
fan 66861
26.6%
apparel 48998
19.5%
golf 33220
13.2%
footwear 14525
 
5.8%
outdoors 9686
 
3.9%
fitness 2479
 
1.0%
discs 2026
 
0.8%
technology 1465
 
0.6%
pet 492
 
0.2%
Other values (4) 1491
 
0.6%

Most occurring characters

ValueCountFrequency (%)
p 167780
13.2%
o 155166
12.2%
a 131470
10.3%
l 84045
 
6.6%
F 83865
 
6.6%
r 73209
 
5.8%
h 71611
 
5.6%
n 71167
 
5.6%
70870
 
5.6%
S 69784
 
5.5%
Other values (20) 291835
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 949267
74.7%
Uppercase Letter 250665
 
19.7%
Space Separator 70870
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 167780
17.7%
o 155166
16.3%
a 131470
13.8%
l 84045
8.9%
r 73209
7.7%
h 71611
7.5%
n 71167
7.5%
e 68683
7.2%
f 33220
 
3.5%
t 27906
 
2.9%
Other values (9) 65010
 
6.8%
Uppercase Letter
ValueCountFrequency (%)
F 83865
33.5%
S 69784
27.8%
A 48998
19.5%
G 33220
 
13.3%
O 9686
 
3.9%
D 2026
 
0.8%
T 1465
 
0.6%
B 767
 
0.3%
P 492
 
0.2%
H 362
 
0.1%
Space Separator
ValueCountFrequency (%)
70870
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1199932
94.4%
Common 70870
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 167780
14.0%
o 155166
12.9%
a 131470
11.0%
l 84045
 
7.0%
F 83865
 
7.0%
r 73209
 
6.1%
h 71611
 
6.0%
n 71167
 
5.9%
S 69784
 
5.8%
e 68683
 
5.7%
Other values (19) 223152
18.6%
Common
ValueCountFrequency (%)
70870
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1270802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 167780
13.2%
o 155166
12.2%
a 131470
10.3%
l 84045
 
6.6%
F 83865
 
6.6%
r 73209
 
5.8%
h 71611
 
5.6%
n 71167
 
5.6%
70870
 
5.6%
S 69784
 
5.5%
Other values (20) 291835
23.0%

market
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
LATAM
51594 
Europe
50252 
Pacific Asia
41260 
USCA
25799 
Africa
11614 

Length

Max length12
Median length6
Mean length6.7997385
Min length4

Characters and Unicode

Total characters1227482
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPacific Asia
2nd rowPacific Asia
3rd rowPacific Asia
4th rowPacific Asia
5th rowPacific Asia

Common Values

ValueCountFrequency (%)
LATAM 51594
28.6%
Europe 50252
27.8%
Pacific Asia 41260
22.9%
USCA 25799
14.3%
Africa 11614
 
6.4%

Length

2023-04-30T12:30:22.467149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-30T12:30:22.616187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
latam 51594
23.3%
europe 50252
22.7%
pacific 41260
18.6%
asia 41260
18.6%
usca 25799
11.6%
africa 11614
 
5.2%

Most occurring characters

ValueCountFrequency (%)
A 181861
14.8%
i 135394
 
11.0%
a 94134
 
7.7%
c 94134
 
7.7%
r 61866
 
5.0%
f 52874
 
4.3%
L 51594
 
4.2%
T 51594
 
4.2%
M 51594
 
4.2%
E 50252
 
4.1%
Other values (10) 402185
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 680670
55.5%
Uppercase Letter 505552
41.2%
Space Separator 41260
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 135394
19.9%
a 94134
13.8%
c 94134
13.8%
r 61866
9.1%
f 52874
 
7.8%
u 50252
 
7.4%
o 50252
 
7.4%
p 50252
 
7.4%
e 50252
 
7.4%
s 41260
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
A 181861
36.0%
L 51594
 
10.2%
T 51594
 
10.2%
M 51594
 
10.2%
E 50252
 
9.9%
P 41260
 
8.2%
U 25799
 
5.1%
S 25799
 
5.1%
C 25799
 
5.1%
Space Separator
ValueCountFrequency (%)
41260
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1186222
96.6%
Common 41260
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 181861
15.3%
i 135394
 
11.4%
a 94134
 
7.9%
c 94134
 
7.9%
r 61866
 
5.2%
f 52874
 
4.5%
L 51594
 
4.3%
T 51594
 
4.3%
M 51594
 
4.3%
E 50252
 
4.2%
Other values (9) 360925
30.4%
Common
ValueCountFrequency (%)
41260
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1227482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 181861
14.8%
i 135394
 
11.0%
a 94134
 
7.7%
c 94134
 
7.7%
r 61866
 
5.0%
f 52874
 
4.3%
L 51594
 
4.2%
T 51594
 
4.2%
M 51594
 
4.2%
E 50252
 
4.1%
Other values (10) 402185
32.8%

order_city
Categorical

Distinct3597
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Santo Domingo
 
2211
New York City
 
2202
Los Angeles
 
1845
Tegucigalpa
 
1783
Managua
 
1682
Other values (3592)
170796 

Length

Max length35
Median length29
Mean length8.5542131
Min length2

Characters and Unicode

Total characters1544198
Distinct characters79
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)< 0.1%

Sample

1st rowBekasi
2nd rowBikaner
3rd rowBikaner
4th rowTownsville
5th rowTownsville

Common Values

ValueCountFrequency (%)
Santo Domingo 2211
 
1.2%
New York City 2202
 
1.2%
Los Angeles 1845
 
1.0%
Tegucigalpa 1783
 
1.0%
Managua 1682
 
0.9%
Mexico City 1484
 
0.8%
Manila 1381
 
0.8%
Philadelphia 1302
 
0.7%
San Francisco 1297
 
0.7%
London 1187
 
0.7%
Other values (3587) 164145
90.9%

Length

2023-04-30T12:30:22.766217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san 6508
 
2.9%
city 5626
 
2.5%
de 3211
 
1.4%
los 2435
 
1.1%
santo 2339
 
1.0%
new 2316
 
1.0%
york 2316
 
1.0%
domingo 2239
 
1.0%
angeles 1857
 
0.8%
tegucigalpa 1783
 
0.8%
Other values (3768) 197270
86.6%

Most occurring characters

ValueCountFrequency (%)
a 197677
 
12.8%
e 116504
 
7.5%
n 115087
 
7.5%
o 111297
 
7.2%
i 94427
 
6.1%
r 85703
 
5.6%
l 76707
 
5.0%
u 57916
 
3.8%
t 56384
 
3.7%
s 56123
 
3.6%
Other values (69) 576373
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1264434
81.9%
Uppercase Letter 225693
 
14.6%
Space Separator 47381
 
3.1%
Dash Punctuation 5982
 
0.4%
Other Punctuation 659
 
< 0.1%
Open Punctuation 21
 
< 0.1%
Close Punctuation 21
 
< 0.1%
Control 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 197677
15.6%
e 116504
 
9.2%
n 115087
 
9.1%
o 111297
 
8.8%
i 94427
 
7.5%
r 85703
 
6.8%
l 76707
 
6.1%
u 57916
 
4.6%
t 56384
 
4.5%
s 56123
 
4.4%
Other values (32) 296609
23.5%
Uppercase Letter
ValueCountFrequency (%)
S 28079
12.4%
C 25625
11.4%
M 23445
 
10.4%
B 16591
 
7.4%
P 15223
 
6.7%
L 14772
 
6.5%
A 13507
 
6.0%
T 10236
 
4.5%
D 9031
 
4.0%
N 7965
 
3.5%
Other values (19) 61219
27.1%
Other Punctuation
ValueCountFrequency (%)
' 509
77.2%
? 137
 
20.8%
. 13
 
2.0%
Space Separator
ValueCountFrequency (%)
47381
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5982
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Control
ValueCountFrequency (%)
’ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1490127
96.5%
Common 54071
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 197677
 
13.3%
e 116504
 
7.8%
n 115087
 
7.7%
o 111297
 
7.5%
i 94427
 
6.3%
r 85703
 
5.8%
l 76707
 
5.1%
u 57916
 
3.9%
t 56384
 
3.8%
s 56123
 
3.8%
Other values (61) 522302
35.1%
Common
ValueCountFrequency (%)
47381
87.6%
- 5982
 
11.1%
' 509
 
0.9%
? 137
 
0.3%
( 21
 
< 0.1%
) 21
 
< 0.1%
. 13
 
< 0.1%
’ 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1528572
99.0%
None 15626
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 197677
 
12.9%
e 116504
 
7.6%
n 115087
 
7.5%
o 111297
 
7.3%
i 94427
 
6.2%
r 85703
 
5.6%
l 76707
 
5.0%
u 57916
 
3.8%
t 56384
 
3.7%
s 56123
 
3.7%
Other values (49) 560747
36.7%
None
ValueCountFrequency (%)
í 4201
26.9%
á 4180
26.8%
ó 2167
13.9%
é 1466
 
9.4%
ã 1354
 
8.7%
ú 1170
 
7.5%
ç 279
 
1.8%
ü 259
 
1.7%
ñ 166
 
1.1%
Á 148
 
0.9%
Other values (10) 236
 
1.5%

order_country
Categorical

Distinct164
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Estados Unidos
24840 
Francia
13222 
México
13172 
Alemania
 
9564
Australia
 
8497
Other values (159)
111224 

Length

Max length31
Median length22
Mean length8.7728272
Min length4

Characters and Unicode

Total characters1583662
Distinct characters61
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowIndonesia
2nd rowIndia
3rd rowIndia
4th rowAustralia
5th rowAustralia

Common Values

ValueCountFrequency (%)
Estados Unidos 24840
 
13.8%
Francia 13222
 
7.3%
México 13172
 
7.3%
Alemania 9564
 
5.3%
Australia 8497
 
4.7%
Brasil 7987
 
4.4%
Reino Unido 7302
 
4.0%
China 5758
 
3.2%
Italia 4989
 
2.8%
India 4783
 
2.6%
Other values (154) 80405
44.5%

Length

2023-04-30T12:30:22.908248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
unidos 24869
 
10.8%
estados 24840
 
10.8%
francia 13222
 
5.7%
méxico 13172
 
5.7%
alemania 9564
 
4.1%
australia 8497
 
3.7%
brasil 7987
 
3.5%
reino 7302
 
3.2%
unido 7302
 
3.2%
china 5758
 
2.5%
Other values (175) 108034
46.9%

Most occurring characters

ValueCountFrequency (%)
a 241810
15.3%
i 166236
 
10.5%
n 119798
 
7.6%
s 117962
 
7.4%
o 109660
 
6.9%
d 83403
 
5.3%
r 66669
 
4.2%
l 62119
 
3.9%
e 53254
 
3.4%
t 50532
 
3.2%
Other values (51) 512219
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1303170
82.3%
Uppercase Letter 229618
 
14.5%
Space Separator 50028
 
3.2%
Open Punctuation 409
 
< 0.1%
Close Punctuation 409
 
< 0.1%
Dash Punctuation 28
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 241810
18.6%
i 166236
12.8%
n 119798
9.2%
s 117962
9.1%
o 109660
8.4%
d 83403
 
6.4%
r 66669
 
5.1%
l 62119
 
4.8%
e 53254
 
4.1%
t 50532
 
3.9%
Other values (22) 231727
17.8%
Uppercase Letter
ValueCountFrequency (%)
E 34032
14.8%
U 33494
14.6%
A 24433
10.6%
I 17168
7.5%
M 16328
7.1%
F 15635
6.8%
C 15397
6.7%
R 13800
 
6.0%
B 12369
 
5.4%
S 8781
 
3.8%
Other values (15) 38181
16.6%
Space Separator
ValueCountFrequency (%)
50028
100.0%
Open Punctuation
ValueCountFrequency (%)
( 409
100.0%
Close Punctuation
ValueCountFrequency (%)
) 409
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1532788
96.8%
Common 50874
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 241810
15.8%
i 166236
 
10.8%
n 119798
 
7.8%
s 117962
 
7.7%
o 109660
 
7.2%
d 83403
 
5.4%
r 66669
 
4.3%
l 62119
 
4.1%
e 53254
 
3.5%
t 50532
 
3.3%
Other values (47) 461345
30.1%
Common
ValueCountFrequency (%)
50028
98.3%
( 409
 
0.8%
) 409
 
0.8%
- 28
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1545222
97.6%
None 38440
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 241810
15.6%
i 166236
 
10.8%
n 119798
 
7.8%
s 117962
 
7.6%
o 109660
 
7.1%
d 83403
 
5.4%
r 66669
 
4.3%
l 62119
 
4.0%
e 53254
 
3.4%
t 50532
 
3.3%
Other values (44) 473779
30.7%
None
ValueCountFrequency (%)
é 14306
37.2%
í 7138
18.6%
á 6253
16.3%
ú 6043
15.7%
ñ 3868
 
10.1%
ó 803
 
2.1%
Á 29
 
0.1%

order_date
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct65752
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
12/14/2016 12:29
 
5
2/22/2015 14:38
 
5
2/17/2016 14:08
 
5
12/31/2016 9:50
 
5
2/11/2016 16:35
 
5
Other values (65747)
180494 

Length

Max length16
Median length15
Mean length14.498989
Min length13

Characters and Unicode

Total characters2617343
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19850 ?
Unique (%)11.0%

Sample

1st row1/31/2018 22:56
2nd row1/13/2018 12:27
3rd row1/13/2018 12:06
4th row1/13/2018 11:45
5th row1/13/2018 11:24

Common Values

ValueCountFrequency (%)
12/14/2016 12:29 5
 
< 0.1%
2/22/2015 14:38 5
 
< 0.1%
2/17/2016 14:08 5
 
< 0.1%
12/31/2016 9:50 5
 
< 0.1%
2/11/2016 16:35 5
 
< 0.1%
12/8/2015 17:54 5
 
< 0.1%
1/31/2016 2:46 5
 
< 0.1%
2/9/2016 14:08 5
 
< 0.1%
11/21/2016 1:29 5
 
< 0.1%
2/12/2016 11:10 5
 
< 0.1%
Other values (65742) 180469
> 99.9%

Length

2023-04-30T12:30:23.040277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7/2/2017 220
 
0.1%
10/27/2015 217
 
0.1%
7/14/2015 213
 
0.1%
5/22/2017 213
 
0.1%
3/18/2016 212
 
0.1%
11/13/2015 212
 
0.1%
6/30/2015 211
 
0.1%
9/18/2016 209
 
0.1%
5/30/2015 209
 
0.1%
3/30/2015 207
 
0.1%
Other values (2557) 358915
99.4%

Most occurring characters

ValueCountFrequency (%)
1 475462
18.2%
2 385354
14.7%
/ 361038
13.8%
0 281809
10.8%
180519
 
6.9%
: 180519
 
6.9%
5 159458
 
6.1%
6 128237
 
4.9%
7 120120
 
4.6%
3 113722
 
4.3%
Other values (3) 231105
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1895267
72.4%
Other Punctuation 541557
 
20.7%
Space Separator 180519
 
6.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 475462
25.1%
2 385354
20.3%
0 281809
14.9%
5 159458
 
8.4%
6 128237
 
6.8%
7 120120
 
6.3%
3 113722
 
6.0%
4 96216
 
5.1%
8 68825
 
3.6%
9 66064
 
3.5%
Other Punctuation
ValueCountFrequency (%)
/ 361038
66.7%
: 180519
33.3%
Space Separator
ValueCountFrequency (%)
180519
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2617343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 475462
18.2%
2 385354
14.7%
/ 361038
13.8%
0 281809
10.8%
180519
 
6.9%
: 180519
 
6.9%
5 159458
 
6.1%
6 128237
 
4.9%
7 120120
 
4.6%
3 113722
 
4.3%
Other values (3) 231105
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2617343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 475462
18.2%
2 385354
14.7%
/ 361038
13.8%
0 281809
10.8%
180519
 
6.9%
: 180519
 
6.9%
5 159458
 
6.1%
6 128237
 
4.9%
7 120120
 
4.6%
3 113722
 
4.3%
Other values (3) 231105
8.8%

order_id
Real number (ℝ)

Distinct65752
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36221.895
Minimum1
Maximum77204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-30T12:30:23.188312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3626.8
Q118057
median36140
Q354144
95-th percentile68596
Maximum77204
Range77203
Interquartile range (IQR)36087

Descriptive statistics

Standard deviation21045.38
Coefficient of variation (CV)0.58101266
Kurtosis-1.1529357
Mean36221.895
Median Absolute Deviation (MAD)18042
Skewness0.032708795
Sum6.5387402 × 109
Variance4.42908 × 108
MonotonicityNot monotonic
2023-04-30T12:30:23.341357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48880 5
 
< 0.1%
3605 5
 
< 0.1%
28265 5
 
< 0.1%
50037 5
 
< 0.1%
27861 5
 
< 0.1%
23412 5
 
< 0.1%
27068 5
 
< 0.1%
27717 5
 
< 0.1%
47273 5
 
< 0.1%
27914 5
 
< 0.1%
Other values (65742) 180469
> 99.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 3
< 0.1%
4 4
< 0.1%
5 5
< 0.1%
7 3
< 0.1%
8 4
< 0.1%
9 3
< 0.1%
10 5
< 0.1%
11 5
< 0.1%
12 5
< 0.1%
ValueCountFrequency (%)
77204 1
< 0.1%
77203 1
< 0.1%
77202 1
< 0.1%
77201 1
< 0.1%
77200 1
< 0.1%
77199 1
< 0.1%
77198 1
< 0.1%
77197 1
< 0.1%
77196 1
< 0.1%
77195 1
< 0.1%

order_item_discount
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1017
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.664741
Minimum0
Maximum500
Zeros10028
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-30T12:30:23.503385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.4000001
median14
Q329.99
95-th percentile62.5
Maximum500
Range500
Interquartile range (IQR)24.59

Descriptive statistics

Standard deviation21.800901
Coefficient of variation (CV)1.0549806
Kurtosis25.231267
Mean20.664741
Median Absolute Deviation (MAD)10
Skewness3.0397955
Sum3730378.4
Variance475.27928
MonotonicityNot monotonic
2023-04-30T12:30:23.660425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10028
 
5.6%
6 4589
 
2.5%
12 4067
 
2.3%
4 3647
 
2.0%
8 3626
 
2.0%
10 3424
 
1.9%
36 3268
 
1.8%
30 3230
 
1.8%
20 3123
 
1.7%
9 2964
 
1.6%
Other values (1007) 138553
76.8%
ValueCountFrequency (%)
0 10028
5.6%
0.100000001 3
 
< 0.1%
0.109999999 15
 
< 0.1%
0.119999997 29
 
< 0.1%
0.150000006 7
 
< 0.1%
0.159999996 7
 
< 0.1%
0.180000007 3
 
< 0.1%
0.200000003 16
 
< 0.1%
0.219999999 6
 
< 0.1%
0.230000004 44
 
< 0.1%
ValueCountFrequency (%)
500 1
 
< 0.1%
400 1
 
< 0.1%
375 25
< 0.1%
360 1
 
< 0.1%
340 1
 
< 0.1%
320 1
 
< 0.1%
300 26
< 0.1%
270 25
< 0.1%
260 1
 
< 0.1%
255 25
< 0.1%

order_item_discount_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10166819
Minimum0
Maximum0.25
Zeros10028
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-30T12:30:23.790452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.039999999
median0.1
Q30.16
95-th percentile0.25
Maximum0.25
Range0.25
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.070415215
Coefficient of variation (CV)0.69259829
Kurtosis-0.90115686
Mean0.10166819
Median Absolute Deviation (MAD)0.059999995
Skewness0.3409276
Sum18353.04
Variance0.0049583025
MonotonicityNot monotonic
2023-04-30T12:30:23.907483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.039999999 10029
 
5.6%
0.150000006 10029
 
5.6%
0.25 10029
 
5.6%
0.200000003 10029
 
5.6%
0.180000007 10029
 
5.6%
0.170000002 10029
 
5.6%
0.050000001 10029
 
5.6%
0.159999996 10029
 
5.6%
0.129999995 10029
 
5.6%
0.119999997 10029
 
5.6%
Other values (8) 80229
44.4%
ValueCountFrequency (%)
0 10028
5.6%
0.01 10028
5.6%
0.02 10028
5.6%
0.029999999 10029
5.6%
0.039999999 10029
5.6%
0.050000001 10029
5.6%
0.059999999 10029
5.6%
0.07 10029
5.6%
0.090000004 10029
5.6%
0.100000001 10029
5.6%
ValueCountFrequency (%)
0.25 10029
5.6%
0.200000003 10029
5.6%
0.180000007 10029
5.6%
0.170000002 10029
5.6%
0.159999996 10029
5.6%
0.150000006 10029
5.6%
0.129999995 10029
5.6%
0.119999997 10029
5.6%
0.100000001 10029
5.6%
0.090000004 10029
5.6%

order_item_id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct180519
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90260
Minimum1
Maximum180519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-30T12:30:24.286567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9026.9
Q145130.5
median90260
Q3135389.5
95-th percentile171493.1
Maximum180519
Range180518
Interquartile range (IQR)90259

Descriptive statistics

Standard deviation52111.491
Coefficient of variation (CV)0.57734867
Kurtosis-1.2
Mean90260
Median Absolute Deviation (MAD)45130
Skewness8.4554661 × 10-18
Sum1.6293645 × 1010
Variance2.7156075 × 109
MonotonicityNot monotonic
2023-04-30T12:30:24.436591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180517 1
 
< 0.1%
42681 1
 
< 0.1%
28953 1
 
< 0.1%
39851 1
 
< 0.1%
173601 1
 
< 0.1%
46791 1
 
< 0.1%
166889 1
 
< 0.1%
31184 1
 
< 0.1%
29248 1
 
< 0.1%
161714 1
 
< 0.1%
Other values (180509) 180509
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
180519 1
< 0.1%
180518 1
< 0.1%
180517 1
< 0.1%
180516 1
< 0.1%
180515 1
< 0.1%
180514 1
< 0.1%
180513 1
< 0.1%
180512 1
< 0.1%
180511 1
< 0.1%
180510 1
< 0.1%

order_item_profit_ratio
Real number (ℝ)

Distinct162
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12064664
Minimum-2.75
Maximum0.5
Zeros1177
Zeros (%)0.7%
Negative33784
Negative (%)18.7%
Memory size1.4 MiB
2023-04-30T12:30:24.595627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2.75
5-th percentile-0.76999998
Q10.079999998
median0.27000001
Q30.36000001
95-th percentile0.47999999
Maximum0.5
Range3.25
Interquartile range (IQR)0.28000002

Descriptive statistics

Standard deviation0.4667956
Coefficient of variation (CV)3.8691142
Kurtosis10.157225
Mean0.12064664
Median Absolute Deviation (MAD)0.16999999
Skewness-2.8935313
Sum21779.01
Variance0.21789814
MonotonicityNot monotonic
2023-04-30T12:30:24.741660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.479999989 9197
 
5.1%
0.349999994 7997
 
4.4%
0.25999999 6577
 
3.6%
0.340000004 6507
 
3.6%
0.469999999 6378
 
3.5%
0.360000014 6108
 
3.4%
0.330000013 5789
 
3.2%
0.49000001 5688
 
3.2%
0.289999992 5478
 
3.0%
0.280000001 5403
 
3.0%
Other values (152) 115397
63.9%
ValueCountFrequency (%)
-2.75 72
 
< 0.1%
-2.700000048 252
0.1%
-2.650000095 90
 
< 0.1%
-2.599999905 181
0.1%
-2.549999952 234
0.1%
-2.5 180
0.1%
-2.450000048 18
 
< 0.1%
-2.349999905 36
 
< 0.1%
-2.299999952 18
 
< 0.1%
-2.25 36
 
< 0.1%
ValueCountFrequency (%)
0.5 2529
 
1.4%
0.49000001 5688
3.2%
0.479999989 9197
5.1%
0.469999999 6378
3.5%
0.460000008 4822
2.7%
0.449999988 3973
2.2%
0.439999998 1390
 
0.8%
0.430000007 1085
 
0.6%
0.419999987 1245
 
0.7%
0.409999996 995
 
0.6%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
99134 
5
20385 
3
20350 
4
20335 
2
20315 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180519
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

Length

2023-04-30T12:30:24.879704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-30T12:30:25.017721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

Most occurring characters

ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Common 180519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 99134
54.9%
5 20385
 
11.3%
3 20350
 
11.3%
4 20335
 
11.3%
2 20315
 
11.3%

sales
Real number (ℝ)

Distinct193
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.7721
Minimum9.9899998
Maximum1999.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-30T12:30:25.160763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9.9899998
5-th percentile49.98
Q1119.98
median199.92
Q3299.95001
95-th percentile399.98001
Maximum1999.99
Range1990
Interquartile range (IQR)179.97001

Descriptive statistics

Standard deviation132.27308
Coefficient of variation (CV)0.64912262
Kurtosis23.936561
Mean203.7721
Median Absolute Deviation (MAD)79.939995
Skewness2.884249
Sum36784735
Variance17496.167
MonotonicityNot monotonic
2023-04-30T12:30:25.316799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.9900055 22372
 
12.4%
399.980011 17325
 
9.6%
199.9900055 15622
 
8.7%
299.980011 13729
 
7.6%
179.9700012 5016
 
2.8%
299.9500122 4988
 
2.8%
119.9800034 4968
 
2.8%
239.9600067 4955
 
2.7%
59.99000168 4893
 
2.7%
50 4432
 
2.5%
Other values (183) 82219
45.5%
ValueCountFrequency (%)
9.989999771 56
 
< 0.1%
11.28999996 271
0.2%
11.53999996 529
0.3%
14.98999977 124
 
0.1%
15.98999977 118
 
0.1%
17.98999977 62
 
< 0.1%
19.97999954 54
 
< 0.1%
19.98999977 176
 
0.1%
21.98999977 51
 
< 0.1%
22 64
 
< 0.1%
ValueCountFrequency (%)
1999.98999 15
 
< 0.1%
1500 442
 
0.2%
999.9899902 10
 
< 0.1%
599.9899902 21
 
< 0.1%
532.5800171 484
 
0.3%
500 15
 
< 0.1%
499.9500122 2510
1.4%
499.75 12
 
< 0.1%
495 14
 
< 0.1%
474.9500122 15
 
< 0.1%

order_region
Categorical

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Central America
28341 
Western Europe
27109 
South America
14935 
Oceania
10148 
Northern Europe
9792 
Other values (18)
90194 

Length

Max length15
Median length14
Mean length12.634304
Min length6

Characters and Unicode

Total characters2280732
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSoutheast Asia
2nd rowSouth Asia
3rd rowSouth Asia
4th rowOceania
5th rowOceania

Common Values

ValueCountFrequency (%)
Central America 28341
15.7%
Western Europe 27109
15.0%
South America 14935
 
8.3%
Oceania 10148
 
5.6%
Northern Europe 9792
 
5.4%
Southeast Asia 9539
 
5.3%
Southern Europe 9431
 
5.2%
Caribbean 8318
 
4.6%
West of USA 7993
 
4.4%
South Asia 7731
 
4.3%
Other values (13) 47182
26.1%

Length

2023-04-30T12:30:25.463830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
europe 50252
13.9%
america 43276
12.0%
asia 31112
 
8.6%
central 30571
 
8.5%
western 27109
 
7.5%
south 26711
 
7.4%
of 18953
 
5.3%
usa 18953
 
5.3%
west 17698
 
4.9%
africa 11614
 
3.2%
Other values (11) 84317
23.4%

Most occurring characters

ValueCountFrequency (%)
e 267374
 
11.7%
r 221631
 
9.7%
202017
 
8.9%
a 185888
 
8.2%
t 170633
 
7.5%
o 129067
 
5.7%
n 114572
 
5.0%
s 105425
 
4.6%
A 104955
 
4.6%
i 104468
 
4.6%
Other values (16) 674702
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1693309
74.2%
Uppercase Letter 385406
 
16.9%
Space Separator 202017
 
8.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 267374
15.8%
r 221631
13.1%
a 185888
11.0%
t 170633
10.1%
o 129067
7.6%
n 114572
6.8%
s 105425
 
6.2%
i 104468
 
6.2%
u 97090
 
5.7%
c 65038
 
3.8%
Other values (7) 232123
13.7%
Uppercase Letter
ValueCountFrequency (%)
A 104955
27.2%
S 71678
18.6%
E 70219
18.2%
C 45735
11.9%
W 44807
11.6%
U 24840
 
6.4%
N 13024
 
3.4%
O 10148
 
2.6%
Space Separator
ValueCountFrequency (%)
202017
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2078715
91.1%
Common 202017
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 267374
12.9%
r 221631
 
10.7%
a 185888
 
8.9%
t 170633
 
8.2%
o 129067
 
6.2%
n 114572
 
5.5%
s 105425
 
5.1%
A 104955
 
5.0%
i 104468
 
5.0%
u 97090
 
4.7%
Other values (15) 577612
27.8%
Common
ValueCountFrequency (%)
202017
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2280732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 267374
 
11.7%
r 221631
 
9.7%
202017
 
8.9%
a 185888
 
8.2%
t 170633
 
7.5%
o 129067
 
5.7%
n 114572
 
5.0%
s 105425
 
4.6%
A 104955
 
4.6%
i 104468
 
4.6%
Other values (16) 674702
29.6%

order_state
Categorical

Distinct1089
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Inglaterra
 
6722
California
 
4966
Isla de Francia
 
4580
Renania del Norte-Westfalia
 
3303
San Salvador
 
3055
Other values (1084)
157893 

Length

Max length36
Median length31
Mean length10.872617
Min length3

Characters and Unicode

Total characters1962714
Distinct characters83
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st rowJava Occidental
2nd rowRajastán
3rd rowRajastán
4th rowQueensland
5th rowQueensland

Common Values

ValueCountFrequency (%)
Inglaterra 6722
 
3.7%
California 4966
 
2.8%
Isla de Francia 4580
 
2.5%
Renania del Norte-Westfalia 3303
 
1.8%
San Salvador 3055
 
1.7%
Nueva York 2753
 
1.5%
Distrito Federal 2559
 
1.4%
Texas 2446
 
1.4%
Nueva Gales del Sur 2370
 
1.3%
Santo Domingo 2211
 
1.2%
Other values (1079) 145554
80.6%

Length

2023-04-30T12:30:25.598860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 9121
 
3.5%
del 8417
 
3.2%
inglaterra 6722
 
2.5%
california 5580
 
2.1%
nueva 5473
 
2.1%
isla 4667
 
1.8%
francia 4580
 
1.7%
san 3965
 
1.5%
sur 3354
 
1.3%
norte-westfalia 3303
 
1.3%
Other values (1171) 208627
79.1%

Most occurring characters

ValueCountFrequency (%)
a 310562
15.8%
n 142494
 
7.3%
i 131317
 
6.7%
e 131085
 
6.7%
r 117395
 
6.0%
o 114621
 
5.8%
l 103549
 
5.3%
83290
 
4.2%
t 80118
 
4.1%
s 75596
 
3.9%
Other values (73) 672687
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1584761
80.7%
Uppercase Letter 268229
 
13.7%
Space Separator 83290
 
4.2%
Dash Punctuation 24466
 
1.2%
Open Punctuation 664
 
< 0.1%
Close Punctuation 664
 
< 0.1%
Other Punctuation 627
 
< 0.1%
Control 13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 310562
19.6%
n 142494
9.0%
i 131317
8.3%
e 131085
8.3%
r 117395
 
7.4%
o 114621
 
7.2%
l 103549
 
6.5%
t 80118
 
5.1%
s 75596
 
4.8%
u 62262
 
3.9%
Other values (35) 315762
19.9%
Uppercase Letter
ValueCountFrequency (%)
C 30372
 
11.3%
S 27262
 
10.2%
A 20489
 
7.6%
P 18540
 
6.9%
M 16660
 
6.2%
N 16027
 
6.0%
I 14188
 
5.3%
B 12615
 
4.7%
G 12427
 
4.6%
L 11348
 
4.2%
Other values (20) 88301
32.9%
Other Punctuation
ValueCountFrequency (%)
? 503
80.2%
' 124
 
19.8%
Control
ValueCountFrequency (%)
Š 8
61.5%
Ž 5
38.5%
Space Separator
ValueCountFrequency (%)
83290
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24466
100.0%
Open Punctuation
ValueCountFrequency (%)
( 664
100.0%
Close Punctuation
ValueCountFrequency (%)
) 664
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1852990
94.4%
Common 109724
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 310562
16.8%
n 142494
 
7.7%
i 131317
 
7.1%
e 131085
 
7.1%
r 117395
 
6.3%
o 114621
 
6.2%
l 103549
 
5.6%
t 80118
 
4.3%
s 75596
 
4.1%
u 62262
 
3.4%
Other values (65) 583991
31.5%
Common
ValueCountFrequency (%)
83290
75.9%
- 24466
 
22.3%
( 664
 
0.6%
) 664
 
0.6%
? 503
 
0.5%
' 124
 
0.1%
Š 8
 
< 0.1%
Ž 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1925211
98.1%
None 37503
 
1.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 310562
16.1%
n 142494
 
7.4%
i 131317
 
6.8%
e 131085
 
6.8%
r 117395
 
6.1%
o 114621
 
6.0%
l 103549
 
5.4%
83290
 
4.3%
t 80118
 
4.2%
s 75596
 
3.9%
Other values (48) 635184
33.0%
None
ValueCountFrequency (%)
í 11965
31.9%
á 10023
26.7%
ó 4698
 
12.5%
é 3332
 
8.9%
ñ 3173
 
8.5%
ã 2031
 
5.4%
ú 918
 
2.4%
ü 380
 
1.0%
à 171
 
0.5%
ô 168
 
0.4%
Other values (15) 644
 
1.7%

order_status
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
COMPLETE
59491 
PENDING_PAYMENT
39832 
PROCESSING
21902 
PENDING
20227 
CLOSED
19616 
Other values (4)
19451 

Length

Max length15
Median length14
Mean length9.6239676
Min length6

Characters and Unicode

Total characters1737309
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOMPLETE
2nd rowPENDING
3rd rowCLOSED
4th rowCOMPLETE
5th rowPENDING_PAYMENT

Common Values

ValueCountFrequency (%)
COMPLETE 59491
33.0%
PENDING_PAYMENT 39832
22.1%
PROCESSING 21902
 
12.1%
PENDING 20227
 
11.2%
CLOSED 19616
 
10.9%
ON_HOLD 9804
 
5.4%
SUSPECTED_FRAUD 4062
 
2.3%
CANCELED 3692
 
2.0%
PAYMENT_REVIEW 1893
 
1.0%

Length

2023-04-30T12:30:25.731893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-30T12:30:25.883924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
complete 59491
33.0%
pending_payment 39832
22.1%
processing 21902
 
12.1%
pending 20227
 
11.2%
closed 19616
 
10.9%
on_hold 9804
 
5.4%
suspected_fraud 4062
 
2.3%
canceled 3692
 
2.0%
payment_review 1893
 
1.0%

Most occurring characters

ValueCountFrequency (%)
E 281578
16.2%
N 197241
11.4%
P 187239
10.8%
O 120617
 
6.9%
C 112455
 
6.5%
T 105278
 
6.1%
D 101295
 
5.8%
M 101216
 
5.8%
L 92603
 
5.3%
I 83854
 
4.8%
Other values (11) 353933
20.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1681718
96.8%
Connector Punctuation 55591
 
3.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 281578
16.7%
N 197241
11.7%
P 187239
11.1%
O 120617
7.2%
C 112455
 
6.7%
T 105278
 
6.3%
D 101295
 
6.0%
M 101216
 
6.0%
L 92603
 
5.5%
I 83854
 
5.0%
Other values (10) 298342
17.7%
Connector Punctuation
ValueCountFrequency (%)
_ 55591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1681718
96.8%
Common 55591
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 281578
16.7%
N 197241
11.7%
P 187239
11.1%
O 120617
7.2%
C 112455
 
6.7%
T 105278
 
6.3%
D 101295
 
6.0%
M 101216
 
6.0%
L 92603
 
5.5%
I 83854
 
5.0%
Other values (10) 298342
17.7%
Common
ValueCountFrequency (%)
_ 55591
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1737309
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 281578
16.2%
N 197241
11.4%
P 187239
10.8%
O 120617
 
6.9%
C 112455
 
6.5%
T 105278
 
6.1%
D 101295
 
5.8%
M 101216
 
5.8%
L 92603
 
5.3%
I 83854
 
4.8%
Other values (11) 353933
20.4%

product_id_card
Real number (ℝ)

Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean692.50976
Minimum19
Maximum1363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-30T12:30:26.051955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile191
Q1403
median627
Q31004
95-th percentile1073
Maximum1363
Range1344
Interquartile range (IQR)601

Descriptive statistics

Standard deviation336.44681
Coefficient of variation (CV)0.48583692
Kurtosis-1.2674939
Mean692.50976
Median Absolute Deviation (MAD)330
Skewness0.13825461
Sum1.2501117 × 108
Variance113196.45
MonotonicityNot monotonic
2023-04-30T12:30:26.214998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365 24515
13.6%
403 22246
12.3%
502 21035
11.7%
1014 19298
10.7%
1004 17325
9.6%
1073 15500
8.6%
957 13729
7.6%
191 12169
6.7%
627 10617
5.9%
1362 838
 
0.5%
Other values (108) 23247
12.9%
ValueCountFrequency (%)
19 64
 
< 0.1%
24 74
 
< 0.1%
35 65
 
< 0.1%
37 262
0.1%
44 305
0.2%
58 29
 
< 0.1%
60 10
 
< 0.1%
61 28
 
< 0.1%
78 63
 
< 0.1%
93 280
0.2%
ValueCountFrequency (%)
1363 650
0.4%
1362 838
0.5%
1361 529
0.3%
1360 357
0.2%
1359 492
0.3%
1358 434
0.2%
1357 208
 
0.1%
1356 362
0.2%
1355 484
0.3%
1354 483
0.3%

product_name
Categorical

Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Perfect Fitness Perfect Rip Deck
24515 
Nike Men's CJ Elite 2 TD Football Cleat
22246 
Nike Men's Dri-FIT Victory Golf Polo
21035 
O'Brien Men's Neoprene Life Vest
19298 
Field & Stream Sportsman 16 Gun Fire Safe
17325 
Other values (113)
76100 

Length

Max length45
Median length43
Mean length35.120004
Min length5

Characters and Unicode

Total characters6339828
Distinct characters66
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSmart watch
2nd rowSmart watch
3rd rowSmart watch
4th rowSmart watch
5th rowSmart watch

Common Values

ValueCountFrequency (%)
Perfect Fitness Perfect Rip Deck 24515
13.6%
Nike Men's CJ Elite 2 TD Football Cleat 22246
12.3%
Nike Men's Dri-FIT Victory Golf Polo 21035
11.7%
O'Brien Men's Neoprene Life Vest 19298
10.7%
Field & Stream Sportsman 16 Gun Fire Safe 17325
9.6%
Pelican Sunstream 100 Kayak 15500
8.6%
Diamondback Women's Serene Classic Comfort Bi 13729
7.6%
Nike Men's Free 5.0+ Running Shoe 12169
6.7%
Under Armour Girls' Toddler Spine Surge Runni 10617
5.9%
Fighting video games 838
 
0.5%
Other values (108) 23247
12.9%

Length

2023-04-30T12:30:26.382034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
men's 77602
 
7.3%
nike 57309
 
5.4%
perfect 49030
 
4.6%
golf 26281
 
2.5%
rip 24515
 
2.3%
deck 24515
 
2.3%
fitness 24515
 
2.3%
cleat 22813
 
2.1%
2 22574
 
2.1%
elite 22309
 
2.1%
Other values (351) 715106
67.0%

Most occurring characters

ValueCountFrequency (%)
887824
 
14.0%
e 733504
 
11.6%
i 378177
 
6.0%
n 350567
 
5.5%
r 340664
 
5.4%
o 293733
 
4.6%
t 274503
 
4.3%
s 263028
 
4.1%
l 230952
 
3.6%
a 230847
 
3.6%
Other values (56) 2356029
37.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4011603
63.3%
Uppercase Letter 1110988
 
17.5%
Space Separator 887824
 
14.0%
Other Punctuation 157902
 
2.5%
Decimal Number 136270
 
2.1%
Dash Punctuation 23040
 
0.4%
Math Symbol 12201
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 126137
11.4%
F 119900
10.8%
P 92713
 
8.3%
D 85981
 
7.7%
M 81289
 
7.3%
N 79408
 
7.1%
C 77993
 
7.0%
T 64000
 
5.8%
G 62759
 
5.6%
R 50142
 
4.5%
Other values (15) 270666
24.4%
Lowercase Letter
ValueCountFrequency (%)
e 733504
18.3%
i 378177
9.4%
n 350567
8.7%
r 340664
8.5%
o 293733
 
7.3%
t 274503
 
6.8%
s 263028
 
6.6%
l 230952
 
5.8%
a 230847
 
5.8%
c 150579
 
3.8%
Other values (14) 765049
19.1%
Decimal Number
ValueCountFrequency (%)
0 44762
32.8%
1 34956
25.7%
2 23239
17.1%
6 18574
13.6%
5 12729
 
9.3%
4 756
 
0.6%
3 643
 
0.5%
8 555
 
0.4%
9 45
 
< 0.1%
7 11
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
' 127155
80.5%
& 17387
 
11.0%
. 12979
 
8.2%
/ 381
 
0.2%
Space Separator
ValueCountFrequency (%)
887824
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23040
100.0%
Math Symbol
ValueCountFrequency (%)
+ 12201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5122591
80.8%
Common 1217237
 
19.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 733504
 
14.3%
i 378177
 
7.4%
n 350567
 
6.8%
r 340664
 
6.7%
o 293733
 
5.7%
t 274503
 
5.4%
s 263028
 
5.1%
l 230952
 
4.5%
a 230847
 
4.5%
c 150579
 
2.9%
Other values (39) 1876037
36.6%
Common
ValueCountFrequency (%)
887824
72.9%
' 127155
 
10.4%
0 44762
 
3.7%
1 34956
 
2.9%
2 23239
 
1.9%
- 23040
 
1.9%
6 18574
 
1.5%
& 17387
 
1.4%
. 12979
 
1.1%
5 12729
 
1.0%
Other values (7) 14592
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6339828
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
887824
 
14.0%
e 733504
 
11.6%
i 378177
 
6.0%
n 350567
 
5.5%
r 340664
 
5.4%
o 293733
 
4.6%
t 274503
 
4.3%
s 263028
 
4.1%
l 230952
 
3.6%
a 230847
 
3.6%
Other values (56) 2356029
37.2%

product_price
Real number (ℝ)

Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.23255
Minimum9.9899998
Maximum1999.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-30T12:30:26.542071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9.9899998
5-th percentile31.99
Q150
median59.990002
Q3199.99001
95-th percentile399.98001
Maximum1999.99
Range1990
Interquartile range (IQR)149.99001

Descriptive statistics

Standard deviation139.73249
Coefficient of variation (CV)0.98937881
Kurtosis23.312997
Mean141.23255
Median Absolute Deviation (MAD)39.999996
Skewness3.1910196
Sum25495159
Variance19525.169
MonotonicityNot monotonic
2023-04-30T12:30:26.697107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.99000168 24820
13.7%
129.9900055 22372
12.4%
50 21035
11.7%
49.97999954 19298
10.7%
399.980011 17325
9.6%
199.9900055 15622
8.7%
299.980011 13729
7.6%
99.98999786 12433
6.9%
39.99000168 11201
6.2%
24.98999977 2339
 
1.3%
Other values (65) 20345
11.3%
ValueCountFrequency (%)
9.989999771 285
 
0.2%
11.28999996 271
 
0.2%
11.53999996 529
 
0.3%
14.98999977 593
 
0.3%
15.98999977 602
 
0.3%
17.98999977 298
 
0.2%
19.98999977 887
 
0.5%
21.98999977 295
 
0.2%
22 308
 
0.2%
24.98999977 2339
1.3%
ValueCountFrequency (%)
1999.98999 15
 
< 0.1%
1500 442
 
0.2%
999.9899902 10
 
< 0.1%
599.9899902 21
 
< 0.1%
532.5800171 484
 
0.3%
461.480011 484
 
0.3%
452.0400085 592
 
0.3%
399.9899902 67
 
< 0.1%
399.980011 17325
9.6%
357.1000061 652
 
0.4%

product_status
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
180519 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180519
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 180519
100.0%

Length

2023-04-30T12:30:26.835142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-30T12:30:26.952164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 180519
100.0%

Most occurring characters

ValueCountFrequency (%)
0 180519
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180519
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 180519
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 180519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 180519
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 180519
100.0%

shipping_date
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct63701
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1/5/2016 5:58
 
10
7/16/2015 10:14
 
10
4/17/2015 22:16
 
10
5/27/2015 6:48
 
10
5/9/2015 18:02
 
10
Other values (63696)
180469 

Length

Max length16
Median length15
Mean length14.501942
Min length13

Characters and Unicode

Total characters2617876
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18557 ?
Unique (%)10.3%

Sample

1st row2/3/2018 22:56
2nd row1/18/2018 12:27
3rd row1/17/2018 12:06
4th row1/16/2018 11:45
5th row1/15/2018 11:24

Common Values

ValueCountFrequency (%)
1/5/2016 5:58 10
 
< 0.1%
7/16/2015 10:14 10
 
< 0.1%
4/17/2015 22:16 10
 
< 0.1%
5/27/2015 6:48 10
 
< 0.1%
5/9/2015 18:02 10
 
< 0.1%
3/13/2016 5:10 10
 
< 0.1%
12/16/2015 2:50 10
 
< 0.1%
6/5/2017 12:25 10
 
< 0.1%
5/8/2015 2:06 10
 
< 0.1%
3/31/2017 19:52 10
 
< 0.1%
Other values (63691) 180419
99.9%

Length

2023-04-30T12:30:27.053180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9/27/2017 266
 
0.1%
5/4/2017 244
 
0.1%
7/9/2017 242
 
0.1%
7/16/2016 240
 
0.1%
8/22/2016 239
 
0.1%
1/15/2017 239
 
0.1%
2/21/2016 237
 
0.1%
9/20/2016 232
 
0.1%
11/4/2015 231
 
0.1%
5/12/2017 228
 
0.1%
Other values (2561) 358640
99.3%

Most occurring characters

ValueCountFrequency (%)
1 475279
18.2%
2 385619
14.7%
/ 361038
13.8%
0 282104
10.8%
180519
 
6.9%
: 180519
 
6.9%
5 159085
 
6.1%
6 128246
 
4.9%
7 120339
 
4.6%
3 113285
 
4.3%
Other values (3) 231843
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1895800
72.4%
Other Punctuation 541557
 
20.7%
Space Separator 180519
 
6.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 475279
25.1%
2 385619
20.3%
0 282104
14.9%
5 159085
 
8.4%
6 128246
 
6.8%
7 120339
 
6.3%
3 113285
 
6.0%
4 96927
 
5.1%
8 68656
 
3.6%
9 66260
 
3.5%
Other Punctuation
ValueCountFrequency (%)
/ 361038
66.7%
: 180519
33.3%
Space Separator
ValueCountFrequency (%)
180519
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2617876
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 475279
18.2%
2 385619
14.7%
/ 361038
13.8%
0 282104
10.8%
180519
 
6.9%
: 180519
 
6.9%
5 159085
 
6.1%
6 128246
 
4.9%
7 120339
 
4.6%
3 113285
 
4.3%
Other values (3) 231843
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2617876
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 475279
18.2%
2 385619
14.7%
/ 361038
13.8%
0 282104
10.8%
180519
 
6.9%
: 180519
 
6.9%
5 159085
 
6.1%
6 128246
 
4.9%
7 120339
 
4.6%
3 113285
 
4.3%
Other values (3) 231843
8.9%

shipping_mode
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Standard Class
107752 
Second Class
35216 
First Class
27814 
Same Day
 
9737

Length

Max length14
Median length14
Mean length12.823969
Min length8

Characters and Unicode

Total characters2314970
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard Class
2nd rowStandard Class
3rd rowStandard Class
4th rowStandard Class
5th rowStandard Class

Common Values

ValueCountFrequency (%)
Standard Class 107752
59.7%
Second Class 35216
 
19.5%
First Class 27814
 
15.4%
Same Day 9737
 
5.4%

Length

2023-04-30T12:30:27.177206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-30T12:30:27.322246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
class 170782
47.3%
standard 107752
29.8%
second 35216
 
9.8%
first 27814
 
7.7%
same 9737
 
2.7%
day 9737
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a 405760
17.5%
s 369378
16.0%
d 250720
10.8%
180519
7.8%
l 170782
7.4%
C 170782
7.4%
S 152705
 
6.6%
n 142968
 
6.2%
r 135566
 
5.9%
t 135566
 
5.9%
Other values (8) 200224
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1773413
76.6%
Uppercase Letter 361038
 
15.6%
Space Separator 180519
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 405760
22.9%
s 369378
20.8%
d 250720
14.1%
l 170782
9.6%
n 142968
 
8.1%
r 135566
 
7.6%
t 135566
 
7.6%
e 44953
 
2.5%
c 35216
 
2.0%
o 35216
 
2.0%
Other values (3) 47288
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
C 170782
47.3%
S 152705
42.3%
F 27814
 
7.7%
D 9737
 
2.7%
Space Separator
ValueCountFrequency (%)
180519
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2134451
92.2%
Common 180519
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 405760
19.0%
s 369378
17.3%
d 250720
11.7%
l 170782
8.0%
C 170782
8.0%
S 152705
 
7.2%
n 142968
 
6.7%
r 135566
 
6.4%
t 135566
 
6.4%
e 44953
 
2.1%
Other values (7) 155271
 
7.3%
Common
ValueCountFrequency (%)
180519
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2314970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 405760
17.5%
s 369378
16.0%
d 250720
10.8%
180519
7.8%
l 170782
7.4%
C 170782
7.4%
S 152705
 
6.6%
n 142968
 
6.2%
r 135566
 
5.9%
t 135566
 
5.9%
Other values (8) 200224
8.6%

Interactions

2023-04-30T12:30:13.536126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:46.018944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:48.064408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:50.239893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:52.352365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:54.450843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:56.427280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:58.485749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:00.778254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:02.763708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:04.819158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:06.924641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:09.249152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:11.415645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:13.690152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:46.157973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:48.207437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:50.389929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:52.506401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:54.593867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:56.568312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:58.869835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:00.925286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:02.917733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:04.966205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:07.070673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:09.404189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:11.577674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:13.837183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:46.312006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:48.341470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:50.533961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:52.646437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:54.727902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:56.701340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:59.005862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:01.061323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:03.064765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:05.110232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:07.213705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:09.566225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:11.725708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:13.999216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:46.468050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:48.492495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:50.688996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:52.807475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:54.880939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:56.854388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:59.167899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:01.212352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:03.226805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:05.283263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:07.368737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:09.736261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:11.881752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:14.149257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:46.616080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:48.638535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:50.844025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:52.954508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:55.020969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:56.998413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:59.317926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:01.353406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:03.368841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:05.434299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:07.517766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:09.894298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:12.032789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:14.287296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:46.749110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:48.772574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:50.986052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:53.105533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:55.151000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:57.133444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:59.449964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:01.481421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:03.501874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:05.572337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:07.653805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:10.039341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:12.167817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:14.435323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:46.888141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:48.911587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:51.132100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:53.248572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:55.284028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:57.273477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:59.586990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:01.616441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:03.635053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:05.719371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:07.794827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:10.181360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:12.313844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:14.595361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:47.040174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:49.063634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:51.290124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:53.404609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:55.432064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:57.422513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:59.733021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:01.761474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:03.781939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:05.880409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:08.183915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:10.345401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:12.473876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:14.743392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:47.180208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:49.366691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:51.437154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:53.542640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:55.566085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:57.554538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:59.872060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:01.890502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:03.912956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:06.021438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:08.324954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:10.489437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:12.623909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:14.897420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:47.321251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:49.506731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:51.581199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:53.687664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:55.703118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:57.696564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:00.013084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:02.033542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:04.048993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:06.168473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:08.466986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:10.637472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:12.768944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:15.053459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:47.469274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:49.651761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:51.736239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:53.837707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:55.847162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:57.853597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:00.163126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:02.173575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:04.203021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:06.316499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:08.619021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:10.793508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:12.921987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:15.206494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:47.615307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:49.794803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:51.888264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:53.985736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:55.988186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:58.016633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:00.310160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:02.312606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:04.356065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:06.464537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:08.762064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:10.943543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:13.070008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:15.360537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:47.765343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:49.943834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:52.042300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:54.136769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:56.135214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:58.190683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:00.463192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:02.459630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:04.509103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:06.618571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:08.921089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:11.101573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:13.226045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:15.515567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:47.912382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:50.090864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:52.195336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:54.290805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:56.280250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:29:58.334715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:00.617217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:02.609664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:04.659123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:06.770599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:09.074127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:11.255606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T12:30:13.374088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-30T12:30:27.475286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
shipping_days_realprofit_per_ordersales_per_customercategory_idcustomer_iddepartment_idorder_idorder_item_discountorder_item_discount_rateorder_item_idorder_item_profit_ratiosalesproduct_id_cardproduct_pricetypeshipping_days_scheduleddelivery_statuslate_delivery_riskcategory_namecustomer_countrycustomer_segmentcustomer_statedepartment_namemarketorder_item_quantityorder_regionorder_statusshipping_mode
shipping_days_real1.000-0.003-0.000-0.0020.004-0.002-0.0020.0010.001-0.002-0.003-0.000-0.002-0.0000.0100.6810.5610.6310.0060.0110.0090.0270.0060.0080.0000.0220.0080.681
profit_per_order-0.0031.0000.4410.0610.0030.0850.0170.196-0.0540.0170.8080.4360.0600.2870.0030.0020.0000.0000.2320.0000.0000.0110.1270.0210.0340.0140.0000.002
sales_per_customer-0.0000.4411.0000.1620.0130.2240.0410.420-0.1330.041-0.0000.9860.1600.6390.0050.0040.0050.0000.4800.0000.0060.0000.2410.0360.2120.0260.0060.004
category_id-0.0020.0610.1621.0000.1330.9080.1350.0730.0000.135-0.0020.1780.9980.1950.0010.0090.0000.0000.9730.0000.0080.0080.6830.1580.2320.1180.0010.009
customer_id0.0040.0030.0130.1331.0000.0610.1330.0030.0020.133-0.0000.0140.1330.0390.0050.0090.0060.0040.5120.0150.0310.0500.3360.1790.0990.1340.0080.009
department_id-0.0020.0850.2240.9080.0611.0000.0630.1020.0000.063-0.0030.2410.9050.2020.0000.0100.0010.0000.9930.0000.0060.0101.0000.1190.2400.0890.0010.010
order_id-0.0020.0170.0410.1350.1330.0631.0000.0190.0011.0000.0010.0440.1310.0820.0150.0100.0090.0060.3370.0120.0060.0260.2420.7530.0980.5510.0120.010
order_item_discount0.0010.1960.4200.0730.0030.1020.0191.0000.7950.019-0.0020.5340.0720.3560.0000.0000.0000.0000.3020.0010.0020.0050.1620.0240.0820.0180.0000.000
order_item_discount_rate0.001-0.054-0.1330.0000.0020.0000.0010.7951.0000.001-0.0020.0000.0010.0000.0000.0000.0040.0060.0000.0030.0000.0000.0000.0000.0000.0000.0020.000
order_item_id-0.0020.0170.0410.1350.1330.0631.0000.0190.0011.0000.0010.0440.1310.0820.0150.0100.0080.0080.2320.0110.0070.0250.1660.7140.0760.5320.0110.010
order_item_profit_ratio-0.0030.808-0.000-0.002-0.000-0.0030.001-0.002-0.0020.0011.000-0.001-0.0020.0000.0000.0000.0000.0000.0020.0000.0000.0010.0000.0050.0000.0000.0000.000
sales-0.0000.4360.9860.1780.0140.2410.0440.5340.0000.044-0.0011.0000.1760.6670.0050.0060.0000.0020.6850.0000.0040.0000.3430.0420.2350.0420.0040.006
product_id_card-0.0020.0600.1600.9980.1330.9050.1310.0720.0010.131-0.0020.1761.0000.1920.0000.0000.0000.0000.9840.0000.0000.0080.7840.1290.2420.1050.0000.000
product_price-0.0000.2870.6390.1950.0390.2020.0820.3560.0000.0820.0000.6670.1921.0000.0050.0080.0000.0040.8070.0000.0020.0000.3940.0590.2290.0600.0030.008
type0.0100.0030.0050.0010.0050.0000.0150.0000.0000.0150.0000.0050.0000.0051.0000.0060.1980.0780.0000.0060.0050.0230.0000.0110.0000.0191.0000.006
shipping_days_scheduled0.6810.0020.0040.0090.0090.0100.0100.0000.0000.0100.0000.0060.0000.0080.0061.0000.3210.4570.0180.0090.0080.0270.0100.0070.0020.0230.0091.000
delivery_status0.5610.0000.0050.0000.0060.0010.0090.0000.0040.0080.0000.0000.0000.0000.1980.3211.0001.0000.0000.0030.0050.0240.0000.0060.0000.0180.5770.321
late_delivery_risk0.6310.0000.0000.0000.0040.0000.0060.0000.0060.0080.0000.0020.0000.0040.0780.4571.0001.0000.0000.0000.0000.0180.0000.0050.0000.0170.2340.457
category_name0.0060.2320.4800.9730.5120.9930.3370.3020.0000.2320.0020.6850.9840.8070.0000.0180.0000.0001.0000.0000.0150.0080.9940.1760.3940.0840.0020.018
customer_country0.0110.0000.0000.0000.0150.0000.0120.0010.0030.0110.0000.0000.0000.0000.0060.0090.0030.0000.0001.0000.0151.0000.0000.0150.0000.0200.0090.009
customer_segment0.0090.0000.0060.0080.0310.0060.0060.0020.0000.0070.0000.0040.0000.0020.0050.0080.0050.0000.0150.0151.0000.0610.0060.0020.0040.0170.0110.008
customer_state0.0270.0110.0000.0080.0500.0100.0260.0050.0000.0250.0010.0000.0080.0000.0230.0270.0240.0180.0081.0000.0611.0000.0090.0300.0000.0260.0220.027
department_name0.0060.1270.2410.6830.3361.0000.2420.1620.0000.1660.0000.3430.7840.3940.0000.0100.0000.0000.9940.0000.0060.0091.0000.1200.2410.0850.0000.010
market0.0080.0210.0360.1580.1790.1190.7530.0240.0000.7140.0050.0420.1290.0590.0110.0070.0060.0050.1760.0150.0020.0300.1201.0000.0271.0000.0110.007
order_item_quantity0.0000.0340.2120.2320.0990.2400.0980.0820.0000.0760.0000.2350.2420.2290.0000.0020.0000.0000.3940.0000.0040.0000.2410.0271.0000.0310.0000.002
order_region0.0220.0140.0260.1180.1340.0890.5510.0180.0000.5320.0000.0420.1050.0600.0190.0230.0180.0170.0840.0200.0170.0260.0851.0000.0311.0000.0170.023
order_status0.0080.0000.0060.0010.0080.0010.0120.0000.0020.0110.0000.0040.0000.0031.0000.0090.5770.2340.0020.0090.0110.0220.0000.0110.0000.0171.0000.009
shipping_mode0.6810.0020.0040.0090.0090.0100.0100.0000.0000.0100.0000.0060.0000.0080.0061.0000.3210.4570.0180.0090.0080.0270.0100.0070.0020.0230.0091.000

Missing values

2023-04-30T12:30:16.159712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-30T12:30:17.300971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

typeshipping_days_realshipping_days_scheduledprofit_per_ordersales_per_customerdelivery_statuslate_delivery_riskcategory_idcategory_namecustomer_citycustomer_countrycustomer_idcustomer_segmentcustomer_statedepartment_iddepartment_namemarketorder_cityorder_countryorder_dateorder_idorder_item_discountorder_item_discount_rateorder_item_idorder_item_profit_ratioorder_item_quantitysalesorder_regionorder_stateorder_statusproduct_id_cardproduct_nameproduct_priceproduct_statusshipping_dateshipping_mode
0DEBIT3491.250000314.640015Advance shipping073Sporting GoodsCaguasPuerto Rico20755ConsumerPR2FitnessPacific AsiaBekasiIndonesia1/31/2018 22:567720213.1100000.041805170.291327.75Southeast AsiaJava OccidentalCOMPLETE1360Smart watch327.7502/3/2018 22:56Standard Class
1TRANSFER54-249.089996311.359985Late delivery173Sporting GoodsCaguasPuerto Rico19492ConsumerPR2FitnessPacific AsiaBikanerIndia1/13/2018 12:277593916.3899990.05179254-0.801327.75South AsiaRajastánPENDING1360Smart watch327.7501/18/2018 12:27Standard Class
2CASH44-247.779999309.720001Shipping on time073Sporting GoodsSan JoseEE. UU.19491ConsumerCA2FitnessPacific AsiaBikanerIndia1/13/2018 12:067593818.0300010.06179253-0.801327.75South AsiaRajastánCLOSED1360Smart watch327.7501/17/2018 12:06Standard Class
3DEBIT3422.860001304.809998Advance shipping073Sporting GoodsLos AngelesEE. UU.19490Home OfficeCA2FitnessPacific AsiaTownsvilleAustralia1/13/2018 11:457593722.9400010.071792520.081327.75OceaniaQueenslandCOMPLETE1360Smart watch327.7501/16/2018 11:45Standard Class
4PAYMENT24134.210007298.250000Advance shipping073Sporting GoodsCaguasPuerto Rico19489CorporatePR2FitnessPacific AsiaTownsvilleAustralia1/13/2018 11:247593629.5000000.091792510.451327.75OceaniaQueenslandPENDING_PAYMENT1360Smart watch327.7501/15/2018 11:24Standard Class
5TRANSFER6418.580000294.980011Shipping canceled073Sporting GoodsTonawandaEE. UU.19488ConsumerNY2FitnessPacific AsiaToowoombaAustralia1/13/2018 11:037593532.7799990.101792500.061327.75OceaniaQueenslandCANCELED1360Smart watch327.7501/19/2018 11:03Standard Class
6DEBIT2195.180000288.420013Late delivery173Sporting GoodsCaguasPuerto Rico19487Home OfficePR2FitnessPacific AsiaGuangzhouChina1/13/2018 10:427593439.3300020.121792490.331327.75Eastern AsiaGuangdongCOMPLETE1360Smart watch327.7501/15/2018 10:42First Class
7TRANSFER2168.430000285.140015Late delivery173Sporting GoodsMiamiEE. UU.19486CorporateFL2FitnessPacific AsiaGuangzhouChina1/13/2018 10:217593342.6100010.131792480.241327.75Eastern AsiaGuangdongPROCESSING1360Smart watch327.7501/15/2018 10:21First Class
8CASH32133.720001278.589996Late delivery173Sporting GoodsCaguasPuerto Rico19485CorporatePR2FitnessPacific AsiaGuangzhouChina1/13/2018 10:007593249.1600000.151792470.481327.75Eastern AsiaGuangdongCLOSED1360Smart watch327.7501/16/2018 10:00Second Class
9CASH21132.149994275.309998Late delivery173Sporting GoodsSan RamonEE. UU.19484CorporateCA2FitnessPacific AsiaGuangzhouChina1/13/2018 9:397593152.4399990.161792460.481327.75Eastern AsiaGuangdongCLOSED1360Smart watch327.7501/15/2018 9:39First Class
typeshipping_days_realshipping_days_scheduledprofit_per_ordersales_per_customerdelivery_statuslate_delivery_riskcategory_idcategory_namecustomer_citycustomer_countrycustomer_idcustomer_segmentcustomer_statedepartment_iddepartment_namemarketorder_cityorder_countryorder_dateorder_idorder_item_discountorder_item_discount_rateorder_item_idorder_item_profit_ratioorder_item_quantitysalesorder_regionorder_stateorder_statusproduct_id_cardproduct_nameproduct_priceproduct_statusshipping_dateshipping_mode
180509PAYMENT340.000000335.980011Advance shipping045FishingCaguasPuerto Rico7CorporatePR7Fan ShopPacific AsiaGuangshuiChina1/16/2016 6:492605264.00.16652020.001399.980011Eastern AsiaHubeiPENDING_PAYMENT1004Field & Stream Sportsman 16 Gun Fire Safe399.98001101/19/2016 6:49Standard Class
180510PAYMENT34165.990005331.980011Advance shipping045FishingCaguasPuerto Rico7CorporatePR7Fan ShopPacific AsiaGuangshuiChina1/16/2016 6:492605268.00.17652010.501399.980011Eastern AsiaHubeiPENDING_PAYMENT1004Field & Stream Sportsman 16 Gun Fire Safe399.98001101/19/2016 6:49Standard Class
180511DEBIT22157.429993327.980011Shipping on time045FishingChula VistaEE. UU.9314ConsumerCA7Fan ShopPacific AsiaChengduChina1/16/2016 6:282605172.00.18651950.481399.980011Eastern AsiaSichuanON_HOLD1004Field & Stream Sportsman 16 Gun Fire Safe399.98001101/18/2016 6:28Second Class
180512DEBIT6486.400002319.980011Late delivery145FishingCaguasPuerto Rico7396Home OfficePR7Fan ShopPacific AsiaChengduChina1/16/2016 6:072605080.00.20651940.271399.980011Eastern AsiaSichuanCOMPLETE1004Field & Stream Sportsman 16 Gun Fire Safe399.98001101/22/2016 6:07Standard Class
180513PAYMENT34119.989998299.989990Advance shipping045FishingLancasterEE. UU.3080Home OfficeOH7Fan ShopPacific AsiaShangháiChina1/16/2016 5:0426047100.00.25651850.401399.980011Eastern AsiaShangháiPENDING_PAYMENT1004Field & Stream Sportsman 16 Gun Fire Safe399.98001101/19/2016 5:04Standard Class
180514CASH4440.000000399.980011Shipping on time045FishingBrooklynEE. UU.1005Home OfficeNY7Fan ShopPacific AsiaShangháiChina1/16/2016 3:40260430.00.00651770.101399.980011Eastern AsiaShangháiCLOSED1004Field & Stream Sportsman 16 Gun Fire Safe399.98001101/20/2016 3:40Standard Class
180515DEBIT32-613.770019395.980011Late delivery145FishingBakersfieldEE. UU.9141CorporateCA7Fan ShopPacific AsiaHirakataJapón1/16/2016 1:34260374.00.0165161-1.551399.980011Eastern AsiaOsakaCOMPLETE1004Field & Stream Sportsman 16 Gun Fire Safe399.98001101/19/2016 1:34Second Class
180516TRANSFER54141.110001391.980011Late delivery145FishingBristolEE. UU.291CorporateCT7Fan ShopPacific AsiaAdelaideAustralia1/15/2016 21:00260248.00.02651290.361399.980011OceaniaAustralia del SurPENDING1004Field & Stream Sportsman 16 Gun Fire Safe399.98001101/20/2016 21:00Standard Class
180517PAYMENT34186.229996387.980011Advance shipping045FishingCaguasPuerto Rico2813ConsumerPR7Fan ShopPacific AsiaAdelaideAustralia1/15/2016 20:182602212.00.03651260.481399.980011OceaniaAustralia del SurPENDING_PAYMENT1004Field & Stream Sportsman 16 Gun Fire Safe399.98001101/18/2016 20:18Standard Class
180518PAYMENT44168.949997383.980011Shipping on time045FishingCaguasPuerto Rico7547ConsumerPR7Fan ShopPacific AsiaNagercoilIndia1/15/2016 18:542601816.00.04651130.441399.980011South AsiaTamil NaduPENDING_PAYMENT1004Field & Stream Sportsman 16 Gun Fire Safe399.98001101/19/2016 18:54Standard Class